Method and device for determining a speed value

ABSTRACT

A method for determining a speed variable that represents the speed of a vehicle, including determining wheel speed variables, each of which describes the speed of a given wheel, determining weighting variables for the individual wheel speed variables, determining a support variable, via averaging, as a function of the wheel speed variables that have been weighted using the weighting variables, and determining the speed variable as a function of the support variable.

FIELD OF THE INVENTION

The present invention relates to a method and a device for determining aspeed variable that represents the speed of a vehicle.

BACKGROUND INFORMATION

German Published Patent Application No. 197 13 251 concerns a method fordetermining a value that describes the speed of a vehicle. According tothis method, the speeds for at least two wheels are determined. Thevalue that describes the speed of the vehicle is determined as afunction of the speed of just one selected wheel. The wheel used todetermine the value that describes the speed of the vehicle is chosen atleast as a function of an operating status of the vehicle, which isdescribed at least via the speeds of at least two wheels and via a valuewhich is determined as a function of at least those speeds and whichrepresents a value that describes the vehicle's acceleration. Thismethod may have the disadvantage that just one of the vehicle's wheelsis used to support the reference speed. As a result, if the system makesa change in the wheel that is selected, there may be a change in thereference speed, although the vehicle's actual speed has not in factchanged.

German Patent No. 44 28 347 C2 apparently concerns a circuit arrangementfor determining the speed of a vehicle. Apparently, the circuitarrangement has a fuzzy system into which wheel speed relativedifference values relative to the speed determined at an earliersampling instant are input as input variables. With the help of thefuzzy system, weighting factors for the individual wheel speeds arecalculated. The weighted mean of the four wheel speeds is calculated andconsidered the speed of the vehicle; i.e., the vehicle's speed isdirectly considered the weighted mean, no support variable beingcalculated.

SUMMARY OF THE INVENTION

An object of an exemplary method and/or an exemplary device of thepresent invention is to provide a method and/or a device for determininga speed variable that represents a vehicle's speed which allows thespeed variable to be calculated more precisely.

The exemplary method according to the present invention relates to amethod for determining a speed variable that represents a vehicle'sspeed. To accomplish this, wheel speed values, each of which describethe speed of a given wheel, are determined. Furthermore, weightingvariables for the individual wheel speed values are determined. Asupport variable is determined via averaging, as a function of the wheelspeed values that have been weighted using the weighting variables. Thespeed variable is determined as a function of the support variable.

The speed variable is determined in an improved manner due to theaveraging via which a support variable is determined, and due to thefact that the speed variable is determined as a function of the supportvariable. Due to the averaging, the speeds of a plurality of wheels areused rather than the speed of just one wheel, the speed of a given wheelbeing weighted as a function of the wheel's suitability for determiningthe speed variable. Because the speed variable is determined as afunction of the support variable—this method is known as “supporting”the speed variable—further weighting can be carried out.

The following further useful advantages are associated with theexemplary method according to the present invention and the exemplarydevice according to the present invention:

more effective calculation of vehicle reference speed (referencecalculation) in vehicle dynamics control systems such as those describedin the automotive technology journal Automobiltechnische Zeitschrift 96,1994, volume 11, pages 674-689, article entitled, “FDR—DieFahrdynamikregelung von Bosch” [Bosch Vehicle Dynamics Control System];

better reference calculation based on full functionality in all drivingsituations in extreme all-wheel vehicles having up to 50%/50% drivedistribution and limited torque coupling in their differentials;

the algorithm for reference calculation is less sensitive to erroneousinput signals that can arise, for example, in the case of pulse wheelssubject to tolerance on revolution sensing elements;

reference calculation is more transferable. The exemplary methodaccording to the present invention can be used with any drive design(rear-wheel drive, front-wheel drive, all-wheel drive with centerdifferential, all-wheel drive with Torsen differential, all-wheel drivewith viscous coupling differential, etc.) without any changes beingnecessary. Information as to whether a wheel is driven or not is notrequired. The system is also suitable for use in off-road vehicles, formaintaining anti-lock brake and drive slip control systems when offroad.

Moreover, a further advantage is that reference calculation can still becarried out even if the steering angle signal, the transverseacceleration signal, the yaw rate signal, or the engine torque signalare no longer present, and it can therefore be used in the back-upsystems of vehicle dynamics control systems. The only input variablesthat are needed are the wheel speeds and the admission pressure set bythe driver. The input variables that are no longer present can becalculated from the wheel speeds via modeling. This advantage arisesfrom the fact that the algorithm for reference calculation is mainlydependent upon the wheel speeds and their derivatives and to a muchlesser extent upon signals Fbij and MMot. Thus the exemplary methodaccording to the present invention can also be used in systems in whichonly the wheel speeds and the wheel braking force in braking situationsare available and only a rough estimate of the yaw rate is available,such as is the case in, for example, the back-up systems of vehicledynamics control systems.

Wheel stability analysis is carried out via a fuzzy logic system, whichis used instead of a crisp logic system such as that described in GermanPublished Patent Application No. 197 13 251. Moreover, the estimatedvehicle speed is corrected using a supporting wheel speed, and inaddition the order of magnitude of this correction is modified on anongoing basis.

With the help of a fuzzy approach, a supporting wheel speed iscalculated from all four wheel speeds. In addition, the probabilityvalue for the suitability of the supporting wheel speed is determinedand can then be used to calculate, for example, the coefficients for aKalman-Bucy filter.

The fact that only half of the fuzzification is taken into account (thecomplement is not taken into account) and that scaling to a probabilityvalue is carried out simplifies the calculations considerably, and alsomeans it is not necessary to carry out defuzzification or to subjectfuzzy sets in vectorial form to logic operations. This means lessprocessing capacity and storage capacity are required.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a first block diagram of the exemplary device according tothe present invention.

FIG. 2 shows a second block diagram of the examplary device according tothe present invention.

FIG. 3 shows a first aspect of the exemplary method according to thepresent invention used in the exemplary device according to the presentinvention.

FIG. 4 shows a second aspect of the exemplary method according to thepresent invention used in the exemplary device according to the presentinvention.

FIG. 5a shows a membership function of the fuzzy control system on whichthe exemplary method according to the present invention is based.

FIG. 5b shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 5c shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 5d shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 5e shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 5f shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 5g shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 5h shows another membership function of the fuzzy control system onwhich the exemplary method according to the present invention is based.

FIG. 6 shows a function used to filter values as part of the fuzzycontrol system.

DETAILED DESCRIPTION

FIG. 1 shows controller 107. This controller is, for example, acontroller used in a vehicle dynamics control system. In this regard,the aforementioned article entitled “FDR—Die Fahrdynamikregelung vonBosch” [Bosch Vehicle Dynamics Control System] is incorporated byreference, as necessary. Various input variables are sent to thecontroller: Transverse acceleration aq, which is determined viatransverse-acceleration sensor 101; the vehicle's yaw rate omega, whichis determined via yaw rate sensor 102; wheel speeds vij, which aredetermined via wheel revolution rate sensors 103 ij; the steering angle,which is determined via steering angle sensor 104; and admissionpressure Pvor set by the driver, which is determined via pressure sensor105. Moreover, clutch torque MMot of the engine, which is made availableby engine control unit 106, is sent to the controller. Starting fromthese input variables, the controller generates trigger signals S1 forthe actuators 108 assigned to it, as a function of the design of thecontrol principle it contains. The actuators may be, for example, meansfor influencing the torque generated by the engine and/or brakes appliedto the vehicle's wheels, the brakes being part of a hydraulic brakesystem, an electro-hydraulic brake system, a pneumatic brake system, anelectro-pneumatic brake system or an electro-mechanical brake system.Signals S2, which provide the controller with information about theactuators' operating status, are sent to the controller from actuators108.

The abbreviation 103 ij is used to denote the wheel revolution ratesensors. Herein, index i indicates whether a front wheel (v) or rearwheel (h) is involved. Index j indicates whether a right (r) or a left(l) vehicle wheel is involved. This abbreviation system is uniformthroughout this document for all values and blocks for which it is used.

FIG. 2 shows controller core 208 and components 201, 202, 203, 204, 205,206, 207, 209 and 210, which are essential to the present invention.

Wheel speed values vij, steering angle delta, the vehicle's yaw rateomega, transverse acceleration aq, the admission pressure Pvor appliedby the driver, the engine's clutch moment MMot, the speed variable vRefthat has been determined and signals S2 are sent to controller core 208.As already mentioned, controller 107 is used to control vehicledynamics, so as to control the vehicle's yaw rate. To accomplish this,yaw rate omega sent to controller core 208 is compared to an associatedsetpoint value. This setpoint value is determined with the help of asuitable vehicle model, as a function of speed variable vRef andsteering angle delta. A setpoint yaw moment that is to be applied to thevehicle in order to reduce the control deviation is determined from thecontrol deviation, which is determined from measured yaw rate omega andthe associated setpoint value. This target yaw moment is converted intotarget slip changes for the individual wheels. Starting from thesetarget slip changes, target slip values for the individual wheels aredetermined and are applied via an underlying brake slip system. Toaccomplish this, a given target slip value is compared to the given slipvalue that is present and the brake of the wheel in question istriggered as a function of the resulting deviation. As is evident fromthis explanation, speed variable vRef is very important. First, it isused in the calculations via which the setpoint value for the yaw rateis determined. Second, speed variable vRef is used to determine the slipvalues. For this reason, it is very important that speed variable vRefbe determined reliably so that the vehicle's actual speed is describedas accurately as possible. In addition to the underlying brake slipcontrol system, controller core 208 also has a subordinate drive slipcontrol system. If necessary, the drive slip control system alsointervenes so as to control the vehicle. The triggering described aboveis carried out via signals S1.

Controller core 208 determines various values and signals that are madeavailable to other blocks. This involves calculations regarding thewheel loads and tire loads present at the wheel or tire in question.Thus braking force Fbij between the tire and the road in the directionof wheel travel is determined in controller core 208 as a function ofthe admission pressure Pvor set by the driver, clutch torque MMot andthe wheel speed value vij in question. To accomplish this, the angularmomentum for the wheel in question is calculated. Braking force Fbij issent from controller core 208 to blocks 201, 204, 206 and 207. Thecontact load, i.e., normal load Fnij of the wheel in question iscalculated in controller core 208 as a function of the admissionpressure Pvor set by the driver, clutch torque MMot, transverseacceleration aq, and yaw rate omega. Contact load Fnij is sent to block201. Braking force Fsij between the given tire and the road,perpendicular to the direction of wheel travel, is determined incontroller core 208 as a function of transverse acceleration aq, yawrate omega, and steering angle delta. Braking force Fsij is sent toblock 204. Furthermore, controller core 208 also handles internallygenerated signals and values which are combined to create signals S3 andsent to block 206. These signals and values are as follows: Value FMotr,which indicates that the engine torque information is no longeravailable, i.e. it indicates that value MMot is not available; valueASR, which indicates that an intervention procedure is being carried outso as to control drive slip at at least one driven wheel; valueMmot*uege, which represents the product of clutch torque MMot of theengine and total gear ratio uege from the transmission and differential,and corresponds to the drive torque generated at the driven wheels bythe engine; value uege is, for example, estimated as a function of theengine rpm and the cardanic rpm; values ABSij, which indicate the wheelat which a control intervention to control brake slip is being carriedout; signal FDRaus, which indicates that the driver has switched thecontrol system over to passive mode by operating a button in thepassenger compartment; and signal EnNlABS, which indicates thatemergency ABS has been activated. If emergency ABS has been activated,for example the break points of the fuzzy sets are modified.

In block 201, a linear slip correction for the given wheels is carriedout as a function of braking force Fbij, contact load Fnij and wheelspeed values vij. Slip-adjusted wheel speeds vij′(k−1) are determinedfrom wheel speeds vij sent to block 201. To accomplish this, first thegiven coefficient of friction that is present for each individual wheelis determined as a function of the given braking force Fbij and thegiven contact load Fnij. Based on the assumption that the wheel inquestion is in the linear part of the μ slip characteristic, the slippresent at the wheel can be determined from the coefficient of frictionthat has been determined and the known tire rigidity rating, via alinear equation. Slip-adjusted wheel speed values vij′(k−1) can bedetermined via the equation${{{vij}^{\prime}\quad ( {k - 1} )} = \frac{vij}{1 - {lambdaij}}},$

lambdaij being the slip that has been determined. Slip-adjusted wheelspeeds vij′(k−1) are sent to block 202.

It is important to note that the figure in brackets next to a givenvalue represents the time (time increment) in question. Thus vij′(k−1)represents the slip-adjusted wheel speeds at time increment (k−1).

In block 202, slip-adjusted wheel speed values vij′(k−1) are subjectedto a geometric transformation to the center point of the vehicle's rearaxle, as a function of yaw rate omega and steering angle delta sent tothe aforementioned block. Because of the geometric transformation, thespeed components contained in the wheel speed values that can beattributed to the vehicle's motion, in particular the vehicle's rotationabout its vertical axis, are eliminated. This elimination is necessaryto ensure that a reliable speed variable that accurately represents thevehicle's current speed can be determined. In the case of the frontwheels, the transformation is carried out using the equation${{{vvj}^{''}\quad ( {k - 1} )} = {\frac{{{vvj}^{\prime}\quad ( {k - 1} )} - {{( {{l1} + {l2}} ) \cdot {omega} \cdot \sin}\quad ({delta})}}{\cos \quad ({delta})} \pm {\frac{Spurw}{2}\quad {omega}}}},$

and in the case of the rear wheels the transformation is carried outusing the equation${{vhj}^{''}\quad ( {k - 1} )} = {{{vhj}^{\prime}\quad ( {k - 1} )} \pm {\frac{Spurw}{2}\quad {{omega}.}}}$

Value 11 represents the distance between the rear axle and the vehicle'scenter of gravity, and value 12 represents the distance between thefront axle and the vehicle's center of gravity. The value Spurwrepresents the vehicle's wheel track width. The plus sign is used forthe wheels on the inside of the curve, and the minus sign is used forthe wheels on the outside of the curve. The transformed wheel speedvariables vij″(k−1) are sent to block 203 from block 202.

In block 204, value axModell(k), which describes the vehicle'sacceleration, is determined based upon braking forces Fbij and Fsij sentto the aforementioned block, and based upon speed variable vRef(k−1) andsteering angle delta. This value is a model acceleration that isdetermined using the principle of linear momentum in the vehicle'slongitudinal direction, the longitudinal forces acting on the vehiclebeing taken into account. Wind resistance forces as well as brakingforces Fbij and Fsij are taken into account as longitudinal forces.Value axModell(k) is determined via the equation:${{axModell}\quad (k)} = {\frac{1}{mf}{\{ {{{( {{Fsvl} + {Fsvr}} ) \cdot \sin}\quad ({delta})} - {{( {{Fbvl} + {Fbvr}} ) \cdot \cos}\quad ({delta})} - ( {{Fbhl} + {Fbhr}} ) - {{{cw} \cdot \rho \cdot A \cdot {vref}}\quad ( {k - 1} )^{2}}} \}.}}$

An estimated value is used as vehicle mass mf. The last term in thebraces represents the wind force acting on a traveling vehicle. ValueaxModell (k) is sent from block 204 to blocks 203 and 206.

In block 203, extrapolated wheel speed variables vij′″(k) are determinedfrom transformed wheel speed variables vij″(k−1), and extrapolated speedvariable vRef′(k) is determined from speed variable vRef′ (k−1). In bothcases, extrapolation is carried out as a function of the vehicle'sestimated acceleration. The vehicle's estimated acceleration iscalculated from model-supported vehicle acceleration axModell(k) andvalue axOff(k−1), which describes an error offset of the model-supportedvehicle acceleration. Extrapolated speed variable vRef′(k) is calculatedvia the equation:

vref′(k)=vref(k−1)+T 0 ·axoff(k−1)+T 0 ·axModell(k).

Extrapolated wheel speed variables vij′″(k) are calculated in a similarmanner. In the above equation, the value T0 represents the samplingperiod. Value vRef′(k) is sent to blocks 206 and 209, and valuesvij′″(k) are sent to block 206.

In block 205, which represents signal processing, first values vijp,which describe first derivative d(vij)/dt of wheel speed variables vijand thus represent the wheel acceleration, are determined from wheelspeed variables vij. Furthermore, second values vijpp, which describesecond derivative d²(vij)/dt² of wheel speeds vij and thus represent thegradients of the given wheel acceleration vijp, are determined.

The first values and the second values are determined with the help ofan FIR filter, for which the general equation is:$y_{n} = {\sum\limits_{k = 0}^{N}\quad {a_{k} \cdot {x_{n - k}.}}}$

Coefficients ak should be selected as a function of the value vijp orvijpp to be determined. It is useful if a fourth-order FIR filter isused, an FIR filter of another order or some other suitable filter meansimplemented in a different way may also be used. Values vijp and vijppare sent from block 205 to block 206.

The adjustment phases, which may have been carried out provided thebrake slip control system is in active mode, and during which individualwheels are under-braked in a controlled manner, are calculated in block207. This is based on the assumption that the wheels which have beenunder-braked in a controlled manner are suitable for determining thewheel speed variables. Thus in block 207, time variable tAdjustij, whichdescribes the duration of the adjustment phase of the wheel in questionassuming that wheel is being under-braked, is determined as a functionof the wheel speed variables vij and the brake force Fbij sent to theaforementioned block.

Calculation of tAdjustij is started as a function of braking force Fbij.In the case of under-braking, the brake pressure and thus braking forceFbij of the wheel in question is lowered. Therefore a decrease inbraking force Fbij constitutes the start instant for calculating thetime variable. Calculation of the time variable ends when the system hasdetermined, as a function of the wheel speed, that the wheel in questionis behaving in stable manner. To accomplish this, it is possible, forexample, to calculate the time behavior of wheel speed variable vij.Time variable tAdjustij is limited by an upper limit. The time variableis sent to block 206.

In block 206, a wheel stability analysis based on a fuzzy logic systemis carried out. A detailed description of the specific method isprovided below in the explanation of FIGS. 3 and 4. Values vij′″(k),vRef′(k), axModell(k), axOff(k−1), Fbij, vijp, vijpp, and tAdjustij,along with various values and signals that are combined to form S3, areused as the input variables for the wheel stability analysis. With thehelp of the fuzzy logic system implemented in block 206, supportvariable vSupp(k) and evaluation variable Fsupp(k) are determined andare sent to block 209.

In block 209, speed variable vRef(k) is corrected as a function of thevalues vSupp(k) and Fsupp(k) sent to the aforementioned block, via theequation:

vref(k)=vref′(k)+kovx·(vSupp(k)−vref(k−1)).

Value kovx is determined in a suitable manner from evaluation variableFsupp(k). The method shown in the equation is known as “supporting”speed variable vref via support variable vSupp. Speed variable vRef(k)determined in block 209 is sent to blocks 208 and 210.

In block 209, value axOff(k) is determined in a similar manner, via theequation:

 axoff(k)·T 0=T 0 axoff(k−1)+koaxoff·(vSupp(k)−vref(k−1)).

This method also constitutes “supporting”. Factor koaxOff is alsodetermined from evaluation variable Fsupp in a suitable manner. ValueaxOff(k) is sent to block 210.

Block 210 is a storage means that is known heretofore, via which thevalues axOff and vref of an earlier time increment are made available,i.e. if, in block 209, speed variable vref for time increment k isdetermined, block 210 supplies the speed variable vRef and the valueaxOff of the previous time increment k−1. The value axOff(k−1) suppliedby block 210 is sent to blocks 203, 206, and 209, and value vRef(k−1)supplied by block 210 is sent to blocks 203, 204 and 209.

Calculation of speed variable vRef in blocks 203 (extrapolation) and 209(correction or supporting), and calculation of value axOff (correctionor support) in block 209, are based on a stationary, time-discreteKalman-Bucy filter. A filter of this kind may be described by theequation:

{circumflex over (x)}(k)=F·{circumflex over(x)}(k−1)+ŷ(k)+K{z(k)−H(F·{circumflex over (x)}(k−1)+ŷ(k)}.

In this equation, {circumflex over (x)}(k) represents the desired stateat time increment k; Ff represents the system's dynamics matrix; y(k)represents the input variable; K represents a weighting matrix; z(k)represents the value used for supporting; and H represents an outputmatrix.

Calculation of the speed variables is based on a second-order system.This system includes a model for speed variable vRef and a model forerror term T0*axOff, which can compensate for unknown errors inacceleration value axModell and the modeling. Per this modeling, thedesired state is x=[vref, axoff·T0] and y=[axModell·T0.0]. It isimportant to note that speed variable vRef represents the vehicle'slongitudinal speed at the center point of the rear axle. In the model,speed variable vRef is the integral of the vehicle acceleration. Thismodel can be described via the following equation:

vref(k)=vref(k−1)+T 0·axoff(k−1)+T 0·axModell(k).

A zero dynamic per the equation

axoff(k)·T 0=axoff(k−1)·T 0

is chosen as the model for the error term T0*axoff. Starting from themodels and definitions described above, the following estimate equationsare used for speed variable vRef and error term T0*axOff:

vref(k)=vref(k−1)+T 0·axoff(k−1)+T0·axModell(k)+kovx·(vSupp(k)−vref(k−1))axoff(k)·T 0=T0·axoff(k−1)+koaxoff·(vSupp(k)−vref(k−1)).

These estimate equations can be derived from the equations described inconnection with blocks 203 and 209.

Values kovx and koaxOff contained in the estimate equations aredetermined from evaluation variable Fsupp via a calculation method for aKalman-Bucy filter, which is an available filter. As can be seen fromthese two estimate equations, the given correction or supporting isinfluenced by factors kovx and koaxOff. To sum up: The magnitude of thecorrection or supporting is influenced via evaluation variable Fsupp.

The magnitude of the correction or supporting of speed variable vref isdetermined with the help of variable Fsupp. A quantity for the systemnoise and measuring noise is determined from Fsupp with the help of afunctional relationship. Matrix K, which changes over time and whichadaptively brings the stationary filter in line with the uncertainty of“measured variable” vsupp, is obtained from this. The functionalrelationship of the noise values of Fsupp and, from that, via the Kalmanmodel, coefficients kovRef and axOff of the Kalman matrix for observingvRef and axOff, can be implemented for a given stationary Kalman filtervia two characteristic curves having as many points as desired.

Below, we describe FIG. 3, which shows, with the help of a flow chart,the wheel stability analysis, which is based on a fuzzy logic system andis carried out in block 206. The wheel stability analysis starts withstep 301, after which processing moves to step 302. In step 302, vehiclemovement variable BEW, which characterizes at least the vehicle'smovement in the longitudinal direction, is supplied. We explain indetail below how vehicle movement variable BEW is supplied, in thedescription of FIG. 4. It is important to note that the vehicle movementvariable is determined at least based upon wheel speed variables vijand/or time derivatives vijp of the wheel speed variables, and/or basedupon axModell, which describes the vehicle's longitudinal acceleration,and/or value ASR, which indicates that a drive slip control interventionhas been carried out. After step 302, processing moves to step 303.

As already mentioned, wheel stability analysis is carried out with thehelp of a fuzzy logic system. A fuzzy logic system may be set up in thefollowing way: Membership functions known as fuzzy sets are defined andset up for a linguistic variable, e.g. the wheel speed or the wheelacceleration. Using the membership functions, the input values sent tothe fuzzy logic system, which constitute crisp values, are fuzzified,i.e. the input values are plotted on a linguistic value scale.Fuzzification may be followed by fuzzy reasoning, i.e. a series ofstatements which, for specific input value combinations, define theaccompanying output value combinations in the form of IF . . . THENrules, are made using linguistic variables, so as to describe thesystem's behavior. After this, defuzzification may be carried out, i.e.a crisp setting value is generated from the fuzzy output values via theindividual manipulated variables. To accomplish this, usually the areabeneath the curves is taken into consideration in conjunction with themembership functions.

However, the approach used in the exemplary method according to thepresent invention differs from other fuzzy logic approaches in thefollowing way: As the desired output variable for the given wheel, whichis to be determined via the wheel stability analysis, is a probablityvalue (weighting variable Fradij) rather than an actuating signal viawhich, for example, a controller could be triggered and whose valuewould have to correspond to a defined response, it is sufficient toanalyze and calculate only the relevant half of the fuzzy set. Thecomplement of the fuzzy set does not have to be taken into account. Thisapproach has the following major advantage: As the linguistic variablesthat have been obtained are already probability values with thecorresponding scaling, there is no need for subsequent defuzzification,which usually involves the greatest expense. Thus only scalar signalshave to be subjected to logic operations. To subject the linguisticvariables to logic operations, for example, the following availableoperators of fuzzy logic systems, are used:${{FuzzyAND}\text{:}\quad y} = {{{Gamma}*{MIN}\quad ( {{x1},{x2}} )} + {( {1 - {Gamma}} )*\frac{{x1} + {x2}}{2}}}$${{FuzzyOR}\text{:}\quad y} = {{{Gamma}*{MAX}\quad ( {{x1},{x2}} )} + {( {1 - {Gamma}} )*\frac{{x1} + {x2}}{2}}}$

with Gamma ε [0, 1]

Herein, values x1 and x2 are input variables; value y is the outputvariable. For the individual logic operations, a fixed, predefined valueis assigned to value Gamma.

The following fuzzy sets are used to calculate the linguistic variables:“small” (FIG. 5a); “smallest” (FIG. 5f); “great” (FIG. 5b); “greatest”(FIG. 5g); “smaller than” (FIG. 5c); “greater than” (FIG. 5d); “closeto” (FIG. 5e); and “closest to” (FIG. 5h). We explain the individualfuzzy sets in greater detail below, in our explanation of FIGS. 5a to 5h.

In step 303, a set of rules is selected as a function of vehiclemovement variable BEW. Thus weighting variables Fradij, which indicatehow suitable a given wheel speed variable vij is for correcting orsupporting speed variable vRef, are calculated, the driving situation,which is described by vehicle movement BEW, being taken into account orconsidered.

If vehicle movement variable BEW indicates that the ‘acceleration’driving situation is present, the following set of rules is selected:

FRadij = (FRadij from straightforward stability analysis (FRStij)) or(FRadij from situation analysis (FRSiij)) or (FRadij from time-relateduncertainty term FRZij)) FRStij = (absolute value of vijpp small) and ((absolute value of vijpp smallest) or (difference between vijppmin andvijppmax small)) and (absolute value of vijp small) and ( (vijp close toaxModell + axOff (not in the case of FMotr)) or (absolute value of axOffgreat (not in the case of FMotr))) and ( ( vij ′ ′ ′ close to vRef′) and( (vij ′ ′ ′ closest to vRef′) or (difference between vijmin and vijmaxsmall))) or ( (vij ′ ′ ′ smaller than vRef′ (vij ′ ′ ′ > 2*vijMin)) and(Fbij small (vij ′ ′ ′ > 2*vijMin)))) FRSiij = (vij ′ ′ ′ close to vRef′and (vij ′ ′ ′ smaller than vRef′) and (number of wheels having (vij ′ ′′ smaller than vRef′) great) FRZij = (uncertainty term great) and ( ((vij ′ ′ ′ smaller than vRef′) and (Fbij small)) or ( (vij ′ ′ ′ closeto vRef′) and (absolute value of vijp small))).

If vehicle movement variable BEW indicates that the ‘deceleration’driving situation is present, the following set of rules is selected:

FRadij = ( (FRadij from straightforward stability analysis (FRStij)) and(FRadij from situation analysis (FRSiij))) or (Fradij from under-brakinganalysis (FRUbFij)) or (FRadij from time-related uncertainty term(FRZij)) FRStij = (absolute value of vijpp small) and ( (absolute valueof vijpp smallest) or (difference between vijppmin and vijppmax small))and (absolute value of vijp small) and (vijp close to axModell + axOff(not in the case of [FMotr and (ABS])) and ( (vij ′ ′ ′ greatest) or(difference between vijmin and vijmax small)) FRSiij = ( (vij ′ ′ ′smaller vRef′) and (vijp small) and ( (vij ′ ′ ′ greatest) or(difference between vijmin and vijmax small) and (absolute value ofvijpp small (vij ′ ′ ′ > 2*vjMin))) and (number of wheels having[absolute value of vijpp very small] great) (vij ′ ′ ′ > 2*vjMin))) or ((vij ′ ′ ′ greater than vRef′) and (Fbij great) and (vijp great) and ((vij ′ ′ ′ close to vRef′) or (difference between vijmin and vijmaxsmall))) or (tAdjustij) great (wheel in adjustment phase) and (vijpsmall) and ( (vij ′ ′ ′ greatest) or (difference between vijmin andvijmax small))) or ( (vij ′ ′ ′ close to vRef′) and (vijp great) and(number of ABS-controlled wheels small)) FRUbFij = (absolute value ofvijp small) and (difference between vijmin and vijmax small) and(difference between vijpmin and vijpmax small) and (difference betweenvijppmin and vijppmax small) and (vij ′ ′ ′ smaller than vRef′ (vij ′ ′′ > 2*vijMin)) FRZij = (uncertainty term great) and (vij ′ ′ ′ smallerthan vRef40 ) and (vij ′ ′ ′ greatest) or (difference between vijmax andvijmin small)

If vehicle movement variable BEW indicates that some other drivingsituation is present, the following set of rules is selected:

Fradij = (FRadij from straightforward stability analysis (FRStij)) or(FRadij from situation analysis (FRSiij, only in the case of FMotr)) or(FRadij from time-related uncertainty term FRZij)) FRStij = (absolutevalue of vijpp small) and ( (absolute value of vijpp smallest) or(difference between vijppmin and vijppmax small)) and (absolute value ofvijp small) and (vijp close to axModell + axOff   (not in the case ofFMotr)) and ( ( vij ′ ′ ′ close to vRef′) and ( (vij ′ ′ ′ closest tovRef′) or (difference between vijmin and vijmax small))) or ( (vij ′ ′ ′smaller than vRef′) and (difference between vijmin and vijmax small)))FRSiij = (vij ′ ′ ′ close to vRef′) and (vij ′ ′ ′ smaller than vRef′)and (number of wheels having (vij ′ ′ ′ smaller than vRef′) great) FRZij= Zero

After step 303, step 304, in which weighting variables Fradij aredetermined for the individual wheels with the help of the selected setof rules, is carried out.

Below, the exemplary method for determining weighting variables Fradijis explained by describing, by way of example, the set of rules used forthe ‘acceleration’ driving situation. Value Fradij determined using thisthis set of rules includes a plurality of individual weighting variablesand individual probability values, i.e. weighting variable Fradij isobtained by subjecting a plurality of individual weighting variables tologic operations. In the present case, weighting variable Fradijincludes the following individual weighting variables: Stabilityvariable FRStij, which is a measure of the stability of the wheel inquestion and describes the straightforward stability of the wheel inquestion, value FRStij being primarily determined as a function of wheelvariable vijpp; situation variable FRSiij, which describes the wheels'current situation—this situation variable contains at least the statusof the given wheel speed variable vij relative to speed variable vRefand/or a value indicating how many of the wheels fulfill a predefinedcondition, this latter value representing the number of wheels whosewheel speed variable vij is smaller than speed variable vRef; qualityvariable FRZij, which is a measure of the quality of weighting variablesFradij determined in the previous time increments. Quality variableFRZij is an integral uncertainty term indicating the probable degree oferror associated with speed variable vRef. The meaning of value FRZijcan be described as follows: If weighting variable Fsupp is small for afairly long time, speed variable vRef is primarily determined viaextrapolation. As the information regarding individual wheels that wouldbe included in speed variable vRef via supporting is only taken intoaccount to a very limited degree, speed variable vRef may be determinederroneously. To be able to spot this shortcoming and counteract it,value FRZij is determined. The weighting variable is predominantly afunction of the stability variable. To determine the stability variable,primarily the rule (absolute value of vijpp small) is used. The smallerthe value vijpp, the greater the resulting value for this rule and thegreater the extent to which this wheel is used to support the speedvariable. However, wheels that behave in this way may also arise due tocontrol interventions. As these wheels should not be consideredstationary and are thus less suitable for supporting speed variablevRef, the other two individual weighting variables via which such wheelscan be recognized are used.

In connection with the ‘deceleration’ driving situation, individualweighting variable FRUBFij, which describes the modification phase orunder-braking present for the wheel in question, is also determined andcalculated.

Some of the rules include, in braces, expressions that are partiallynegated and partially non-negated. An example of a rule having a negatedexpression is [vijp close to axModell+axoff {not in the case of FMotr}].Value FMotr in the braces indicates whether the engine torqueinformation is still available. Rule (vijp close to axModell+axoff) isonly used if the engine torque information is still available. Othernegated expressions are interpreted in a similar manner. One example ofa non-negated instance is [vij′″ smaller than vRef′{vij′″>2*vijMin}].The rule (vij′″ smaller than vRef′) is only used if the condition{vij′″>2*vijMin} is fulfilled or satisfied.

The value of weighting variable Fradij is defined as follows: First, thegiven values for stability variable FRStij, situation variable FRSiij,and quality variable FRZij are determined. Here, we explain the methodused with reference to quality variable FRZij. Quality variable FRZijincludes the following rules:

(Uncertainty term great)  (E1)

(vij′″ smaller than vRef′)  (E2)

(Fbij small)  (E3)

 (vij′″ close to vRef′)  (E4)

and

(absolute value of vijp small)  (E5).

First, rules (E1) to (E5) are used. This is carried out with the help ofthe membership functions shown in FIG. 5. Herein, the rules and thus theindividual weighting variables and weighting variable may have any valuebetween 0 and 1.

Below, we describe, by way of example, the use of rule (E3), to whichthe membership function shown in FIG. 5a relates. Braking force Fbij isthe input variable. To determine the result of this rule, the value ofbraking force Fbij is plotted on the abscissa. The result of the rule isthe point where the vertical line that passes through the abscissa pointintersects with the membership function. The results of the other rulesare determined in a similar manner. The individual results are subjectedto logic operations in conjunction with quality variable FRZij in asimilar manner using the aforementioned fuzzy operators. Stabilityvariable FRStij and situation variable FRSiij are determined in asimilar manner. The three values FRStij, FRSiij and FRZij are eachsubjected to logic operations in conjunction with weighting variableFradij, using the fuzzy operator “or”.

To sum up: First, the results for the rules are obtained with the helpof the membership functions. Using these results, the individualweighting variables are determined with the help of fuzzy operators, andthen the weighting variable is determined from those values, also withthe help of the fuzzy operators.

The aforementioned explanation also applies in a similar manner to thesets of rules that are used for the ‘deceleration’ driving situation and“other driving situations.”

Below, we provide an explanation of various rules:

before using the rule [difference between vijppmin and vijppmax small],first the values vijppmin and vijppmax must be determined, and then thedifference between them must be calculated.

the rule [number of wheels having [vij′″ smaller than vRef′] great] isused as follows: First, the result of the rule [vij′″ smaller thanvRef′] is determined for all wheels. The results are then added up. Theresulting total is used, with the help of the membership function shownin FIG. 5b.

when rule [number of ABS-controlled wheels small] is used, first thesystem uses the signals or values ABSij contained in S3 to determine howmany wheels are being subjected to ABS control. Then the resulting valueis used, using the membership function shown in FIG. 5a.

The minimum values and maximum values used in the rules, e.g. vijppminand vijppmax, may be determined using suitably appropriate and availablestorage means and comparison means. The input variables that are usedfor the wheel stability analysis are as follows: axOff, vij′″, vijp,vijpp, Fbij, and tAdjustij. In the tables showing the various sets ofrules, in the case of each individual value we have not shown the timeincrement in question; this is for the sake of clarity.

After step 304, step 305, in which values f(Fradij) are determined, iscarried out. This calculation is carried out with the help of thefunction shown in FIG. 6, value x being the input signal for whichcalculations are performed with the help of the curve of function f(x).The curve of function f(x) has various different sections: Betweenabscissa value 0 and Kx1, function f(x) has the value 0; betweenabscissa values Kx1 and Kx2, function f(x) has gradient Kx2/(Kx2−Kx1);and between abscissa values Kx2 and 1, function f(x) has the gradient ofthe bisector that passes through the origin. This means that smallvalues of the input variable are filtered out (values between 0 and Kx1)or suppressed (values between Kx1 and Kx2).

After step 305, step 306, in which support variable vSupp and evaluationvariable Fsupp are determined, is carried out. The support variable isdetermined as a function of the values f (Fradij), i.e. from theweighting variables Fradij determined via function f (x) and fromslip-corrected and transformed wheel speed variables vij′″. This iscarried out, for example, per the function:${{vSupp} = \frac{\sum\limits^{\quad}\quad ( {{vij}^{\prime''}*( {f\quad ({FRadij})} )^{2}} )}{\sum\limits^{\quad}\quad ( {f\quad ({FRadij})} )^{2}}},$

summation being carried out over all wheels.

Evaluation variable Fsupp is determined as a function of value f(Fradij), for example per the relationship:

Fsupp=a*MAX(f(FRadij))+(1−a)*MIN(Σ(f(FRadij)),1),

with a ε [0, 1].

Evaluation variable Fsupp represents the probability that value vSupp issuitable for supporting speed variable vref. Value Fsupp is primarilydependent on the maximum Fradij value, i.e. on the weighting variable ofthe wheel which is most suitable for determining value vSupp and whichthus makes the greatest contribution to value vSupp. Summation andcalculation of the maximum are carried out over all wheels.

After step 306, step 307, in which the ‘uncertainty term’ value isdetermined, is carried out. The ‘uncertainty term’ value is determinedas a function of the weighting variable, for example per therelationship

 uncertainty term=MAX (0, uncertainty term (k−1)−KAb*(Fsupp−P _(—)FrefP) for Fsupp≧P _(—) FrefP−KAn*(Fsupp−P _(—) FrefP) for Fsupp<P _(—)FrefP)

with P_FrefP ε [0, 1].

The ‘uncertainty term’ value is used to estimate the possible degree oferror associated with speed variable vref. The ‘uncertainty term’ valueis an integral measure of the fact that for a fairly long period of timesmall values of Fsupp have not resulted in any correction of the speedvariable. In the above equation, KAn and KAb are magnification factorsto be applied, where KAn<<KAb.

After step 307, in step 308 speed variable vref is determined as afunction of support variable vSupp and evaluation variable Fsupp. Thiscalculation is carried out using the equations set forth earlier in thisdocument. The values axModell and axoff as well as the two values vSuppand Fsupp are used to calculate speed variable vref.

After step 308, step 302 is carried out again, i.e. starting from thecurrent time increment speed variable vref for the subsequent timeincrement is calculated.

Below, we explain FIG. 4. This figure shows how vehicle movementvariable BEW is supplied, as carried out in step 302. The procedure forsupplying and calculating vehicle movement variable BEW starts in step401, which is followed by step 402, in which value BEW is initialized,i.e. the value 0 is assigned to it. After step 402, step 403, in which,with the help of the queries

deceleration = (not ASR) and ( ( (mean vij ′ ′ ′ < vRef′) and (mean vijp< p_aRadmVerz) or (axModell < P_axVerz) or (number of wheels in ABS > 2)

the system determines whether the ‘deceleration’ driving situation ispresent for the vehicle. If, in step 403, the system determines that the‘deceleration’ driving situation is present, after step 403 step 404 iscarried out. In this step, the value V is assigned to value BEW. Afterstep 404, step 408, in which the procedure for supplying value BEW isended, is carried out. However, if in step 403, the system determinesthat the ‘deceleration’ driving situation is not present, after step 403step 405, in which the system determines whether the driving situation‘acceleration’ is present for the vehicle by using the queries

acceleration = (not deceleration) and ( ( (mean vij ′ ′ ′ > vRef′) and(mean vijp > P_aRadmAccel)) or ( ( (MMot*ueGe > P_MkaHalfAccel) or ( ASRand (axModell > P_axAccel))) and (axModell + axOff > P_axAccel)) or (FdrAus and (MMot*ueGe > P_MkaHalfAccelFdrAus)))

is carried out. If, in step 405, the system determines that the‘acceleration’ driving situation is present, after step 405 step 406 iscarried out. In this step, the value B is assigned to value BEW. Afterstep 406, step 408 is carried out. However, if, in step 405, the systemdetermines that the ‘acceleration’ driving situation is not present,which is equivalent of saying that neither the ‘deceleration’ drivingsituation nor the ‘acceleration’ driving situation but rather an “otherdriving situation” is present, after step 405 step 407, in which thevalue S is assigned to value BEW, is carried out. After step 407, step408 is carried out.

The procedure for determining the driving situation carried out in stepsin steps 403 and 405 is carried out via a discrete logic system. ValuesP_aRadmVerz, P_axVerz, P_aRadmAccel, P_MKaHalfAccel, P_axAccel, andP_MKaHalfAccelFdrAus that are used are parameters that are to beapplied. Basically, in steps 403 and 405 the wheel speeds, the timederivatives of the wheel speeds, a longitudinal acceleration value, andvalue ASR, which indicates whether, in accordance with a drive slipcontrol system, interventions are being carried out, are used. Thedriving situation may also be determined using the fuzzy method.

Below, we describe FIG. 5 (FIGS. 5a to 5 h). In these Figures, variousmembership functions (fuzzy sets) used in the wheel stability analysisare shown. Basically, there are three different categories of membershipfunction. The first category contains the two fuzzy sets “small” and“great”, which are parameterized in a fixed manner. In the case of thesetwo fuzzy sets, values Kx1 and Kx2, and thus also the transition betweenthe ordinate values 0 and 1, are predefined and fixed. The secondcategory of fuzzy sets contains the fuzzy sets “smaller than”, “greaterthan”, and “close to”. These fuzzy sets are parameterized fuzzy setsthat are influenced by state values that change over time. This isdescribed below with reference to the “smaller than” fuzzy set. Forexample, if a check is performed to determine whether “wheel speedvariable vij is smaller than speed vref”, in this instance state valuevref constitutes the reference for the fuzzy set, i.e. the value ofspeed variable vref defines the value ‘reference’ on the abscissa. Theposition of the two break points is then determined by the differencevalues Kx1 and Kx2, which are predefined and fixed. The procedure issimilar for the other two fuzzy sets. The third category of fuzzy setscontains the fuzzy sets “smallest”, “greatest” and “closest to”. Thesefuzzy sets are adaptive fuzzy sets that are parameterized solely by therelative position of the individual wheel-specific state values and aretherefore dynamic. In these three instances all of the vehicle's wheelsshould be taken into account when determining the assignment function,as a function of the linguistic values. Described below is how a fuzzyset is generated using the example of the “smallest” fuzzy set. If, forexample, a check is to be performed to determine whether wheel speedvariable vij is smallest, first the two values MAX(x) and MIN(x) aredetermined. MAX(x) constitutes the maximum value and MIN(x) the minimumvalue among all the wheel speed variables. Starting from these twovalues, weighted difference Kx1* (MAX(x)−MIN(x)) is calculated. Thisprocedure constitutes scaling, as the fuzzy set only has the abscissarange that is predefined by the input variable for which calculationsare to be performed. Relative input variables are then used as the inputvariables for this fuzzy set.

The following should be deemed to apply to the equations andrelationships set forth in this application: The equations andrelationships indicate given concrete specifications for calculations,in accordance with which a value is determined from various inputvariables. Nevertheless, it should be deemed that the application alsoincludes the general relationship for each of these equations andrelationships, i.e. the general relationship between the input variablesand the value to be determined, as distinct from the concretespecifications for calculations.

What is claimed is:
 1. A method for determining a speed variablerepresenting a speed of a vehicle having wheels, comprising: determiningwheel speed variables, each of the wheel speed variables describing aspeed of a corresponding one of the wheels; determining a respectiveweighting variable for each of the wheel speed variables; determining asupport variable as a function of weighted averaging of the wheel speedvariables, each of the wheel speed variables being weighted by therespective weighting variable; and determining the speed variable as afunction of the support variable.
 2. The method of claim 1, wherein:each of the weighting variables is determined as a function of apredefined set of rules, the predefined set of rules being selectablefrom a plurality of predefined sets of rules as a function of a vehiclemovement variable, the vehicle movement variable indicating at least amovement of the vehicle in a longitudinal direction; and the vehiclemovement variable is determined at talk least as a function of: thewheel speed variables; time derivatives of the wheel speed variables; afunction of a value corresponding to a longitudinal acceleration of thevehicle; and a value indicating that a drive slip control system hasperformed at least one intervention procedure.
 3. The method of claim 2,wherein the vehicle movement variable is useable for distinguishingamong a plurality of vehicle movement types, the plurality of vehiclemovement types including at least one of: a decelerating vehiclemovement, for which a first set of rules is selectable; an acceleratingvehicle movement, for which a second set of rules is selected; andanother vehicle movement that is neither decelerating nor accelerating,for which a third set of rules is selectable.
 4. The method of claim 1,wherein the step of determining the respective weighting variableincludes: accounting for at least one modification phase that is presentfor wheels; and determining time variables representing a duration ofadjustment phases, the time variable being for determining the weightingvariables.
 5. The method of claim 1, further comprising: determining arespective wheel variable for each of the wheels, each of the respectivewheel variables representing a second time derivative of the speed ofthe corresponding one of the wheels; wherein each respective weightingvariable includes at least one of: a stability variable determined atleast as a function of the respective wheel variable and representing ameasure of a wheel stability; a situation variable including at leastone of a status of the respective wheel speed variable relative to thespeed variable and a value indicating a number of the wheels satisfyinga predefined condition; and a quality variable representing a measure ofa quality of the respective weighting variable determined in a previoustime increment.
 6. The method of claim 1, wherein the speed variableincludes a first component determined as a function of a valuecorresponding to a vehicle acceleration and a second componentdetermined as a function of the support variable, the valuecorresponding to the vehicle acceleration including a longitudinalacceleration component determinable by a model and including an errorcomponent determined as a function of the support variable.
 7. A methodfor determining a speed variable representing a speed of a vehiclehaving wheels, comprising: determining wheel speed variables, each ofthe wheel speed variables describing a speed of a corresponding one ofthe wheels; determining a respective weighting variable for each of thewheel speed variables; determining a support variable as a function ofweighted averaging of the wheel speed variables, each of the wheel speedvariables being weighted by the respective weighting variable; anddetermining the speed variable as a function of the support variable;wherein: each respective weighting variable is determined using at leastone of a non-crisp logic system and a fuzzy logic system; the at leastone weighting variable represents at least one probability value; andthe at least one probability value indicates a suitability of the wheelfor determining the at least one speed variable.
 8. The method of claim7, wherein: the at least one of the non-crisp logic system and the fuzzylogic system is based on a plurality of membership functions, theplurality of membership functions forming at least one rule forperforming at least one calculation for at least one input variable; atleast one of the plurality of membership functions has a fixed,predefined characteristic; and at least one other of the plurality ofmembership functions has a variable characteristic; and the plurality ofmembership functions are for at least one category, the at least onecategory including at least one of “small”, “great”, “smaller than”,“greater than”, “close to”, “smallest”, “greatest”, and “closest to”. 9.The method of claim 8, wherein each of the plurality of membershipfunctions having a variable characteristic adapts to at least one of theat least one input variable and a reference variable, the referencevariable being useable as a comparison value in the at least onecalculation for the at least one input variable, the reference variableforming a basis for the at least one rule.
 10. A method for determininga speed variable representing a speed of a vehicle having wheels,comprising: determining wheel speed variables, each of the wheel speedvariables describing a speed of a corresponding one of the wheels;determining a respective weighting variable for each of the wheel speedvariables; determining a support variable as a function of weightedaveraging of the wheel speed variables, each of the wheel speedvariables being weighted by the respective weighting variable; anddetermining the speed variable as a function of the support variable;wherein each respective weighting variable is determined as a functionof at least one of: the wheel speed variables; first wheel variablesrepresenting first time derivatives of the wheel speed variables; secondwheel variables representing second time derivatives of the wheel speedvariables; and at least one wheel braking force variable correspondingto a prevailing braking force between a tire and a road.
 11. A methodfor determining a speed variable representing a speed of a vehiclehaving wheels, comprising: determining wheel speed variables, each ofthe wheel speed variables describing a speed of a corresponding one ofthe wheels; determining a respective weighting variable for each of thewheel speed variables; determining a support variable as a fuinction ofweighted averaging of the wheel speed variables, each of the wheel speedvariables being weighted by the respective weighting variable;determining the speed, variable as a function of the support variable;determining, using each of the wheel speed variables, a respectiveslip-corrected wheel speed variable accounting for a slip present at thecorresponding one of the wheels; determining from the slip-correctedwheel speed variables transformed wheel speed variables, the transformedwheel speed variables accounting for a vehicle movement, the vehiclemovement including the vehicle movement described by a yaw rate and asteering angle; and determining from the transformed wheel speedvariables extrapolated wheel speed variables, for which an accelerationof the vehicle is accounted for; wherein these further steps areperformed prior to the step of determining the respective weightingvariable.
 12. A method for determining a speed variable representing aspeed of a vehicle having wheels, comprising: determining wheel speedvariables, each of the wheel speed variables describing a speed of acorresponding one of the wheels; determining a respective weightingvariable for each of the wheel speed variables; determining a supportvariable as a function-of weighted averaging of the wheel speedvariables, each of the wheel speed variables being weighted by therespective weighting variable; determining the speed variable as afunction of the support variable; and at least one of suppressing andfiltering small values among the weighting variables, before furtherprocessing the weighting variables.
 13. A method for determining a speedvariable representing a speed of a vehicle having wheels, comprising:determining wheel speed variables, each of the wheel speed variablesdescribing a speed of a corresponding one of the wheels; determining arespective weighting variable for each of the wheel speed variables;determining a support variable as a function of weighted averaging ofthe wheel speed variables, each of the wheel speed variables beingweighted by the respective weighting variable; determining the speedvariable as a function of the support variable; determining anevaluation variable as a function of the weighting variables, theevaluation variable being a measure of a suitability of the supportvariable for determining the speed variable; determining a magnitude ofa support to be performed as a function of the evaluation variable; andcalculating, as a function of the evaluation variable, a factor fordetermining a component of the speed variable associated with thesupport variable.
 14. The method of claim 13, further comprising:calculating, as a function of the evaluation variable, an uncertaintyterm value, wherein the uncertainty term value corresponds to a qualityof the speed variable and determines the weighting variables.
 15. Amethod for determining a speed variable representing a speed of avehicle having wheels, comprising: determining wheel speed variables,each of the wheel speed variables describing a speed of a correspondingone of the wheels; determining a respective weighting variable for eachof the wheel speed variables; determining a support variable as afunction of weighted averaging of the wheel speed variables, each of thewheel speed variables being weighted by the respective weightingvariable; and determining the speed variable as a function of thesupport variable; wherein each of the-weighting variables is determinedas a function of a predefined set of rules, the predefined set of rulesbeing selectable from a plurality of predefined sets of rules as afunction of a vehicle movement variable, the vehicle movement variableindicating at least a movement of the vehicle in a longitudinaldirection; wherein the vehicle movement variable is determined at leastas a function of: the wheel speed variables; time derivatives of thewheel speed variables; a function of a value corresponding to alongitudinal acceleration of the vehicle; and a value indicating that adrive slip control system has performed at least one interventionprocedure; wherein the vehicle movement variable is useable fordistinguishing among a plurality of vehicle movement types, theplurality of vehicle movement types including at least one of: adecelerating vehicle movement, for which a first set of rules isselectable; an accelerating vehicle movement, for which a second set ofrules is selected; and another vehicle movement that is neitherdecelerating nor accelerating, for which a third set of rules isselectable; and wherein: FRadij = (FRadij from straightforward stabilityanalysis (FRStij)) or (FRadij from situation analysis (FRSiij)) or(FRadij from time-related uncertainty term FRZij)) FRStij = (absolutevalue of vijpp small) and ( (absolute value of vijpp smallest) or(difference between vijppmin and vijppmax small)) and (absolute value ofvijp small) and ( (vijp close to axModell + axOff (not in the case ofFMotr)) or (absolute value of axOff great (not in the case of FMotr)))and ( ( vij ′ ′ ′ close to vRef′) and ( (vij ′ ′ ′ closest to vRef′) or(difference between vijmin and vijmax small))) or ( (vij ′ ′ ′ smallerthan vRef′ (vij ′ ′ ′ > 2*vijMin)) and (Fbij small (vij ′ ′ ′ >2*vijMin)))) FRSiij = (vij ′ ′ ′ close to vRef′) and (vij ′ ′ ′ smallerthan vRef′) and (number of wheels having (vij ′ ′ ′ smaller than vRef′)great) FRZij = (uncertainty term great) and ( ( (vij ′ ′ ′ smaller thanvRef′) and (Fbij small)) or ( (vij ′ ′ ′ close to vRef′) and (absolutevalue of vijp small))) is the first set of rules; Fradij = ( (FRadijfrom straightforward stability analysis (FRStij)) and (FRadij fromsituation analysis (FRSiij))) or (Fradij from under-braking analysis(FRUbFij)) or (FRadij from time-related uncertainty term (FRZij)) FRStij= (absolute value of vijpp small) and ( (absolute value of vijppsmallest) or (difference between vijppmin and vijppmax small)) and(absolute value of vijp small) and (vijp close to axModell + axOff(notin the case of [FMotr and (ABS])) and ( (vij ′ ′ ′ greatest) or(difference between vijmin and vijmax small)) FRSiij = ( (vij ′ ′ ′smaller vRef′) and (vijp small) and ( (vij ′ ′ ′ greatest) or(difference between vijmin and vijmax small) and (absolute value ofvijpp small (vij ′ ′ ′ > 2*vjMin)) and (number of wheels having [amountvijpp very small] great) (vij ′ ′ ′ > 2*vjMin))) or ( (vij ′ ′ ′ greaterthan vRef′) and (Fbij great) and (vijp great) and ( (vij ′ ′ ′ close tovRef′) or (difference between vijmin and vijmax small))) or(tadjustmentij) great (wheel in adjustment phase) and (vijp small) and ((vij ′ ′ ′ greatest) or (difference between vijmin and vijmax small)))or ( (vij ′ ′ ′ close to vRef′) and (vijp great) and (number ofABS-controlled wheels small)) FRUbFij = (absolute value of vijp small)and (difference between vijmin and vijmax small) and (difference betweenvijpmin and vijpmax small) and (difference between vijppmin and vijppmaxsmall) and (vij ′ ′ ′ smaller than vRef′ (vij ′ ′ ′ > 2*vijMin)) FRZij =(uncertainty term great) and (vij ′ ′ ′ smaller than vRef′) and (vij ′ ′′ greatest) or (difference between vijmax and vijmin small) is thesecond set of rules; and Fradij = (FRadij from straightforward stabilityanalysis (FRStij)) or (FRadij from situation analysis (FRSiij, only inthe case of FMotr)) or (FRadij from time-related uncertainty termFRZij)) FRStij = (absolute value of vijpp small) and ( (absolute valueof vijpp smallest) or (difference between vijppmin and vijppmax small))and (absolute value of vijp small) and ( (vijp close to axModell + axOff(not in the case of FMotr) and ( ( vij ′ ′ ′ close to vRef′) and ( (vij′ ′ ′ closest to vRef′) or (difference between vijmin and vijmaxsmall))) or ( (vij ′ ′ ′ smaller than vRef′) and (difference betweenvijmin and vijmax small))) FRSiij = (vij ′ ′ ′ close to vRef′) and (vij′ ′ ′ smaller than vRef′) and (number of wheels having (vij ′ ′ ′smaller than vRef′) great) FRZij = Zero

is the third set of rules.
 16. A method for determining a speed variablerepresenting a speed of a vehicle having wheels, comprising: determiningwheel speed variables, each of the wheel speed variables describing aspeed of a corresponding one of the wheels; determining a respectiveweighting variable for each of the wheel speed variables; determining asupport variable as a function of weighted averaging of the wheel speedvariables, each of the wheel speed variables being weighted by therespective weighting variable; determining the speed variable as afunction of the support variable; and using at least one of adeceleration driving query set and an acceleration driving query set,wherein: (not ASR) and ( (  (mean vij″′ < vRef′ ) and  (mean vijp <p_aRadmVerz) or (axModell < P_axVerz) or (number of wheels in ABS > 2)

is the deceleration driving query set, the deceleration driving queryset determining whether a deceleration driving situation is present; and(not deceleration) and ( ( (mean vij ′ ′ ′ > vRef′) and (mean vijp >P_aRadmAccel)) or ( ( (MMot*ueGe > P_MkaHalfAccel) or ( ASR and(axModell > P_axAccel))) and (axModell + axOff > P_axAccel)) or ( FdrAusand (MMot*ueGe > P_MkaHalfAccelFdrAus)))

is the acceleration driving query set, the acceleration driving queryset determining whether an acceleration driving situation is present.17. A device for determining a speed variable representing a speed of avehicle having wheels, the device comprising: means for determiningwheel speed variables, each of the wheel speed variables describing aspeed of a corresponding on of the wheels; means for determining arespective weighting variable for each of the wheel speed variables;means for determining a support variable as a function of weightedaveraging of the wheel speed variables, each of the wheel speedvariables being weighted using the respective weighting variable; andmeans for determining the speed variable as a function of the supportvariable.
 18. A device for determining a speed variable representing aspeed of a vehicle having wheels, the device comprising: a firstarrangement for determining wheel speed variables, each of the wheelspeed variables describing a speed of a corresponding one of the wheels;a second arrangement for determining a respective weighting variable foreach of the wheel speed variables; a third arrangement for determining asupport variable as a function of weighted averaging of the wheel speedvariables, each of the wheel speed variables being weighted using therespective weighting variable; and a fourth arrangement for determiningthe speed variable as a function of the support variable.